太原理工大学学报2024，Vol.55Issue(2)：223-230,8.DOI:10.16355/j.tyut.1007-9432.20230272
煤矿采空区智能充填深度神经网络算法
Deep Neural Network Algorithm of Intelligent Backfilling in Goaf
摘要
Abstract
[Purposes]The intelligent filling of goaf is an important direction of green,safe,intelli-gent,and efficient mining of coal resources,and the key lies in intelligent decision-making and control of gangue-filling process in underground goaf.[Methods]To realize this,the stress and deformation of surrounding rock after filling are taken as monitoring indicators,and a deep neural network algo-rithm for intelligent filling in goaf is established,which can calculate and analyze the stope stress and surrounding rock deformation of different filling schemes under corresponding conditions by entering key basic parameters such as coal seam burial depth,thickness,working face length,and thickness of the direct roof.By using simulation results of FLAC3D under 400 different conditions as a dataset,the intelligent filling deep neural network algorithm was trained and tested,and compared with other three different algorithms.[Findings]The results show that:the intelligent filling deep neural network algorithm is generally better than the random forest algorithm,decision tree algorithm,and multiple linear regression algorithm,and the average caculation speed of each group of data is only 0.013 s;The average error values of key parameters such as the maximum deformation of the roof plate,the peak pressure of the coal wall of working face,and the advanced support distance of roadway calculat-ed by the intelligent filling deep neural network algorithm are between 2％～8％;The algorithm is tested according to the actual conditions of the site,and the results are basically consistent with the ac-tual results on the site,indicating that the algorithm is scientific and feasible.[Conclusions]This study is of great significance and value to green and intelligent mining.关键词
采空区充填/绿色开采/智能充填/深度神经网络算法Key words
goaf filling/green mining/intelligent filling/deep neural network algorithm分类
矿业与冶金引用本文复制引用
周忠斌,梁卫国,郭凤岐,阎雾龙..煤矿采空区智能充填深度神经网络算法[J].太原理工大学学报,2024,55(2):223-230,8.基金项目
国家区域创新发展联合基金重点项目(U22A20167) （U22A20167）
